401-3936-00L Data Analytics for Non-Life Insurance Pricing

Abstract	We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests and gradient boosting machines.
Objective	The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction.
Content	We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - neural networks - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting
Lecture notes	The lecture notes are available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308
Prerequisites / Notice	This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch Good knowledge in probability theory, stochastic processes and statistics is assumed.